Investigating and minimizing bias in generative LLMs and generative image models

Bias in generative AI models, including both LLMs and generative image models, is a complex issue that requires careful investigation and mitigation strategies. Bias can manifest as unintended stereotypes, inaccuracies, and exclusions in the generated outputs, often stemming from biased datasets and model architectures. Recognizing and addressing these biases is crucial to creating equitable and trustworthy AI systems.

At its core, algorithmic or model bias refers to systematic errors that lead to preferential treatment or unfair outcomes for certain groups. In generative AI, this can appear as gender, racial, or socioeconomic biases in outputs, often mirroring societal stereotypes. For example, an LLM may produce content that reinforces these biases, reflecting the historical and societal biases present in its training data.

Let us again revisit our hypothetical fashion retailer, StyleSprint. Consider...